title: Module-based breast cancer classification.
abstract: The reliability and reproducibility of gene biomarkers for classification of cancer patients has been challenged due to measurement noise and biological heterogeneity among patients. In this paper, we propose a novel module-based feature selection framework, which integrates biological network information and gene expression data to identify biomarkers not as individual genes but as functional modules. Results from four breast cancer studies demonstrate that the identified module biomarkers. achieve higher classification accuracy in independent validation datasets. Are more reproducible than individual gene markers. Improve the biological interpretability of results. Are enriched in cancer 'disease drivers'.
authors.affiliation: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA. Zhang.Yuji@mayo.edu
authors.forename: Yuji
authors.initials: Y
authors.lastname: Zhang
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xuan
authors.forename: Robert
authors.initials: R
authors.lastname: Clarke
authors.forename: Habtom W
authors.initials: HW
authors.lastname: Ressom
mesh_terms: D001943:Breast Neoplasms / Q000145:classification / Q000235:genetics*; D005260:Female; D005819:Genetic Markers; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D060066:Protein Interaction Maps; D015203:Reproducibility of Results; D015398:Signal Transduction; D059467:Transcriptome
chemical_list: D005819:Genetic Markers